With the increasing availability of large-scale spatiotemporal data from location-based services, trajectory publishing has become essential for data-driven analysis and intelligent applications. However, insufficient protection of trajectory location data may result in the disclosure of user privacy and social relationship information. To address this issue, we propose a semantic-aware privacy-preserving trajectory data publishing scheme (SAPP). First, a sliding-window algorithm is employed to extract stay points as key semantic locations and to generate a uniformly sampled set of candidate obfuscation points. Then, a semantic-aware scoring function is designed to probabilistically select candidate points that preserve semantics while avoiding sensitive regions. Furthermore, SAPP computes the sensitivity of each location based on semantic frequency and dynamically allocates the privacy budget. Finally, random noise is added to candidate trajectories using the Laplace mechanism. Through a dual-perturbation mechanism, spatial correlations in sensitive regions are weakened. Security analysis and experimental results further demonstrate that, compared with existing approaches, SAPP reduces TPPS and SFRR by up to 18% and 14%, respectively, indicating stronger resistance against trajectory inference and semantic leakage attacks while maintaining high data utility and time efficiency.

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